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Trust in AI and Its Role in the Acceptance of AI Technologies
Open accessAbstract
As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants’ attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Two studies extend the technology acceptance model (TAM) by adding trust in AI, finding that trust indirectly affects intention to use AI through perceived usefulness and attitude. Study 2 identifies two trust dimensions—human-like trust and functionality trust—with functionality trust showing a greater total impact on usage intention than human-like trust.
Key findings
- In Study 1 (N = 312 college students), the path model explained 52% of variance in usage intention (R² = .52); attitude was the strongest direct predictor of behavioral intention (β = .62, p < .001), and trust had a significant indirect effect through perceived usefulness and attitude. Total effects on usage intention were perceived ease of use β = .63, perceived usefulness β = .46, and trust β = .32 (all p < .001).
- In Study 2 (N = 640 representative U.S. adults), exploratory factor analysis yielded two trust dimensions: human-like trust in AI (six items) and functionality trust in AI (five items). The TAM results replicated: attitude predicted intention (β = .65, p < .001), perceived ease of use had total effects of β = .71 in both models, and perceived usefulness had total effects of β = .57 and β = .56 (all p < .001).
- Both trust dimensions had significant indirect effects on intention to use smart technologies, but functionality-related trust had a greater total impact on usage intention (β = .42, p < .001) than human-like trust (β = .36, p < .001).
Methods
- Study 1: online survey conducted October–December 2020 with 312 undergraduate students from a large Midwest university (56% female; ages 18–29, median 21; 96% had used at least one AI voice assistant). Path analysis was conducted in R using lavaan with maximum likelihood estimation and bootstrapped 95% confidence intervals (k = 1,000).
- Study 2: online survey conducted April 2021 using a national Qualtrics panel with quota sampling for gender, age, and race based on U.S. Census data; 640 respondents participated (50.1% female, mean age = 46.43, SD = 17.83; 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other). Trust items were factor analyzed, yielding two dimensions, and two separate path models tested human-like trust and functionality trust using the same analytic approach as Study 1.
- Measures: perceived ease of use, perceived usefulness, attitude, and behavioral intention were adapted from TAM scales; Study 1 trust in AI voice assistants used four created items; Study 2 trust items were constructed around benevolence/helpfulness, integrity/reliability, and competence/functionality.
Limitations
- Stated in the paper: the studies focused on consumer AI products (voice assistants and smart technologies) associated with lower risk, so findings may not generalize to high-stakes AI such as self-driving cars or healthcare AI; trust is dynamic and may differ across stages such as initial versus ongoing trust; the path models are correlational and cannot guarantee causal effects; and online panel respondents may not be truly representative because they have computer and internet access, skewing toward higher technology acceptance.
- Evident from the text: Study 1 used a convenience sample of college students, limiting generalizability; both studies relied on cross-sectional self-report survey data, so causal direction among trust, perceived usefulness, attitude, and intention cannot be established from these data alone.
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94 Own result · 27 Not checked yet · 18 Own interpretation · 15 Own method · 7 No basis given
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In the paper
“The perceived usefulness of an AI voice assistant also positively influenced users' attitude toward it ( 𝛽 = .42, p < .001)”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The validated model presents a high explanatory power, with 52% of variance explained ( R 2 = .52).”
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“the path model demonstrated a good fit with the data: 𝜒 2 (1) = .32, p = .58; RMSEA = .00 [.00, .12]; SRMR = .01; CFI = 1.00; TLI = 1.01.”
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In the paper
“trust perception ( 𝛽 = .39, p < .001)”
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“Moreover, trust positively predicted perceived usefulness.”
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In the paper
“In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust.”
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In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
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In the paper
“willingness to use it ( 𝛽 = .20, p < .01)”
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In the paper
“A path analysis confirmed that trust had a significant effect on the intention to use AI, which operated through perceived usefulness and participants' attitude toward voice assistants.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“attitude ( 𝛽 = .26, p < .001)”
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In the paper
“All other measures of fit (CFI above .95, SRMR below .05, and RMSEA below .08) showed that the models were adequate in reproducing covariances among the variables.”
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In the paper
“The models fit the data well: human trust in AI, 𝜒 2 (1) = 4.92, p = .03; RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98; technology-related trust in AI, 𝜒 2 (1) = 4.85, p = .03 RMSEA = .08 [.02, .15]; SRMR = .01; CFI = 1.00; TLI = .98.”
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In the paper
“The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The values in Table 2 indicate that the constructs are moderately correlated.”
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In the paper
“Trust also predicted positive attitudes, which in turn influenced usage intention (H4b).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“perceived ease of use had the largest total effect on usage intention ( 𝛽 = .63; p < .001), followed by perceived usefulness ( 𝛽 = .46; p < .001) and trust perception ( 𝛽 = .32; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“In addition, we found that trust can influence and is influenced by the factors included in the TAM.”
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“Trust was associated with increased perceived usefulness, which in turn increased usage intention (H4a).”
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In the paper
“Items from the three constructs were factor analyzed, which yielded a two-factor structure, based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation. The items measuring benevolence and integrity were grouped together, and competence was treated as another dimension.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Perceived ease of use, perceived usefulness, and attitude were positive predictors of usage intention.”
plaindirect- Asserted: passes — The authors put it forward as true.
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“The results of indirect effects revealed that all mediated paths depicted were statistically significant, including the hypothesized (H5) paths (see Table 3).”
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In the paper
“Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust.”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, trust predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In both path models, participants' intention to use a smart technology was predicated on its perceived ease of use (total effect 𝛽 = .71; p < .001; 𝛽 = .71; p < .001) and perceived usefulness (total effect 𝛽 = .57; p < .001; 𝛽 = .56; p < .001).”
plaindirect- Asserted: passes — The authors put it forward as true.
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“both trust dimensions ( 𝛽 = .56, p < .001; 𝛽 = .51, p < .001)”
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In the paper
“Additionally, the results indicate that the two dimensions of trust in AI-the human-like trust dimension and the functionality dimension -significantly predict perceived usefulness and positive attitude toward smart technology, which in turn, predict greater usage intention.”
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In the paper
“The two types of trust were associated with perceived usefulness, which predicted greater usage intention (H4a).”
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In the paper
“Finally, trust dimensions predicted perceived usefulness, which in turn increased positive attitudes and usage intention (H4c).”
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In the paper
“Trust also predicted positive attitudes, which in turn affected usage intention (H4b).”
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“In Study 1 and Study 2, we found that perceived ease of use and perceived usefulness were essential determinants for the use of AI technologies, although perceived ease of use had a consistently greater impact on the acceptance of these technologies.”
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“This dimension was a significant predictor of the acceptance of technology, as was the functionality dimension.”
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“both the human-like and the functionality dimensions of trust had statistically significant indirect effects on the intention to use AI smart technologies, thus supporting H5.”
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In the paper
“Factor 1: Human-like trust in AI”
- Also stated in: § Tables and Figures · plain
“Factor 2: Functionality trust in AI”
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- Also stated in: § Tables and Figures · plain
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This finding suggests that trust in technology is a dynamic concept that is still evolving and variable based on the context and the characteristics of the trusted agent.”
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“Also, current voice assistants are designed to be more human-like than previous attempts, and many individuals are communicating with voice assistants as part of their everyday life in the same way they interact with other humans (Sundar et al., 2017), making trust more relevant.”
What the sources say
- Not checked yetcites Sundar et al., 2017
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“Unlike traditional technologies that rely on user input and the execution of rules programmed by a human, AI implies certain levels of autonomy.”
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“Trust is vital to understanding interpersonal interaction, as it contributes to perceived reliability and integrity.”
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In the paper
“In the current study, building on previous work regarding trust, we focus on the two main dimensions of trust in AI: 1) human-like trust in AI, and 2) functionality trust in AI.”
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In the paper
“trust has been examined as a key factor in the acceptance of emerging technologies (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
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In the paper
“The finding is in line with studies that have tested the impact of these constructs on the adoption of various forms of technology (e.g., Beldad & Hegner, 2018; Choi & Ji, 2015).”
What the sources say
- Not checked yetcites Beldad & Hegner, 2018
- Not checked yetcites Choi & Ji, 2015
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In the paper
“The latter dimension pertains to the competency and expertise of the technological features.”
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In the paper
“The constructs 'perceived ease of use' and 'perceived usefulness' were measured with five items, each adapted from the original TAM scale (Davis, 1989).”
What the sources say
- Not checked yetcites Davis, 1989
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In the paper
“The anthropomorphic nature of voice assistants could make trust more critical.”
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“To ensure the representativeness of participants, we adopted a quota sampling method that set quotas for gender, age, and race based on U.S. Census data.”
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In the paper
“This finding suggests that to motivate individuals to adopt or continue to use an AI voice assistant, they should find the technology easy to use and useful.”
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- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Past studies have applied interpersonal trust to human technology relationships (Calhoun et al., 2019), especially when the technology has human-like characteristics (Gillath et al., 2021).”
What the sources say
- Not checked yetcites Calhoun et al., 2019
- Not checked yetcites Gillath et al., 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For human-like trust in AI, trust is in the agent, not in the specific actions and operations of the agent (J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites J. D. Lee & See, 2004
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Ninety-six percent of participants reported that they had used at least one type of AI voice assistant.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similar to trust in people and trust in technology, trust in automation is summarized in three categories: 1) performance, 2) process, and 3) purpose.”
What the sources say
- Not checked yetcites Lee and See (2004)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Voice assistants are designed to be more human-like (McLean & Osei-Frimpong, 2019), with their voice feature encouraging users to adopt the same social responses that they habitually use in their interpersonal relationships (Reeves & Nass, 1996).”
What the sources say
- Not checked yetcites McLean & Osei-Frimpong, 2019
- Not checked yetcites Reeves & Nass, 1996
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Behavioral intention to use or continue to use an AI voice assistant was measured with three items from the TAM 2 scale (Venkatesh & Davis, 2000).”
What the sources say
- Not checked yetcites Venkatesh & Davis, 2000
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The former dimension pertains to the social and cultural values of the algorithms and the values and ethics that undergird the design of the AI technology.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“We consider that in the context of AI, both trust in people and trust in technology are relevant, since AI technologies are often depicted as being capable of human qualities, including reasoning and motivations, which can induce high expectations and initial trust (Glikson & Woolley, 2020).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“McKnight et al. (2011) explained that trust in technology is qualitatively different from trust in people.”
What the sources say
- Not checked yetcites McKnight et al. (2011)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The TAM is a theoretical framework for explaining the technology usage behavior that has been validated in different technologies across various populations”
What the sources say
- Not checked yetcites Venkatesh & Davis
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 640 respondents (50.1% female, age M = 46.43, SD = 17.83, 65.9% White, 12% Black or African American, 12% Hispanic, 5.9% Asian, 4.4% other) participated in the study.”
plainn = 640- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“For instance, Glikson and Woolley (2020) organized the literature on trust in AI based on the factors that shaped users' cognitive and emotional trust.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lee & Moray (1992)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Further, human-like trust may explain emotional trust and emotional attachment to technology.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our findings provide further support for the utility of the TAM in explaining the acceptance of AI technologies and the added contribution of trust within the model.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The distinctive perceptions of human-like trust and functionality trust align with studies that propose multiple dimensions and factors of trust in AI and automation (Glikson & Woolley, 2020; Hoff & Bashir, 2015; J. D. Lee & See, 2004).”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
- Not checked yetcites Hoff & Bashir, 2015
- Not checked yetcites J. D. Lee & See, 2004
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“As past research has already confirmed, trust in AI can be built through embedding specific principles in the design and governance of AI, as suggested in recent publications using the fairness, accountability, transparency, explainability framework (Shin, 2020b; Shin & Park, 2019).”
What the sources say
- Not checked yetcites Shin, 2020b
- Not checked yetcites Shin & Park, 2019
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Still, an opposite direction of the relationship was also found to be valid in another study (Shin, 2020a).”
What the sources say
- Not checked yetcites Shin, 2020a
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“We proposed trust as an antecedent of perceived usefulness based on the findings from the past TAM literature (K. Wu et al., 2011).”
What the sources say
- Not checked yetcites K. Wu et al., 2011
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“First, we focused on AI technology applications in the form of consumer products (i.e., voice assistants and smart technologies), which tend be associated with lower risks.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Hoff and Bashir (2015) suggested three layers of trust in automation (dispositional trust, situational trust, and learned trust) based on their review of trust in AI.”
What the sources say
- Not checked yetcites Hoff & Bashir, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It appears the duality of AI as both a functional and social technology must be considered when designing trustworthy AI.”
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The cognitive construct involves the rational evaluation of AI while emotional trust is typically influenced by irrational factors.”
What the sources say
- Not checked yetcites Glikson & Woolley, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This impact may be because consumers tend to believe that AI technologies are complicated and difficult to use.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Mayer et al., (1995)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The model explains that when people consider an AI technology to be easy to use, they are more likely to trust the technology and consider it useful.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These findings are consistent with Study 1 and prior literature (Gefen et al., 2003; I.-L. Wu & Chen, 2005).”
What the sources say
- Not checked yetcites Gefen et al., 2003
- Not checked yetcites I.-L. Wu & Chen, 2005
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“| Basis of trust in people Mayer et al., (1995) | Basis of trust in technology Lankton et al. (2015) | Basis of trust in automation Lee & Moray (1992) |”
What the sources say
- Not checked yetcites Lankton et al. (2015)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .92 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .89 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .92 | 𝛼 = .90”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .98 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .81 | N/A”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“𝛼 = .90 | 𝛼 = .91”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“N/A | 𝛼 = .91 |”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper relies on this without support.
Perception of fairness and justice is associated with human trust in AI.
“Moreover, the perception of fairness and justice will be associated with human trust in AI.”
The paper relies on this without support.
Trust is relevant throughout the lifecycle of a technology.
“Indeed, trust is relevant throughout the lifecycle of a technology.”
The paper relies on this without support.
Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.
“Users who participate in an experience with technology build ideas about whether it is useful and easy to use based on their experience of use.”
The paper relies on this without support.
Trust is a dynamic concept that varies depending on the stage of technology.
“Second, trust is a dynamic concept that varies depending on the stage of technology.”
The paper relies on this without support.
Participants in an online survey are likely to have greater access to and experience with various forms of information technology.
“Participants in an online survey are likely to have greater access to and experience with various forms of information technology.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Regarding reliance, Choung et al. (2023) demonstrated that trust significantly influences reliance intentions and Kahr et al. (2024) found that trust and reliance on AI advice were significantly higher when the AI model was highly accurate and that trust increased over repeated interactions.”
From Investigating appropriate reliance on AI-Based decision support systems: the role of expertise, trust, and self-confidence (2025) · cites this paper as Choung et al., 2023
Experimental — accuracy not yet measuredIn the citing paper
“These findings underscore that trust has a significant impact on perceived ease of use, perceived usefulness, and attitudes toward GenAI.”
From Artificial intelligence and communication technologies in academia: faculty perceptions and the adoption of generative AI (2025) · cites this paper as Choung et al., 2022
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Not confirmedχ²(1) = 0.32reported p = .58 · recomputed p = .572
“χ 2 (1) = .32, p = .58”
- Consistentχ²(1) = 4.92reported p = .03 · recomputed p = .027
“χ 2 (1) = 4.92, p = .03”
- Consistentχ²(1) = 4.85reported p = .03 · recomputed p = .028
“χ 2 (1) = 4.85, p = .03”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Measures
“based on a principal components exploratory factor analysis (EFA) with an Oblimin rotation.”
§ Tables and Figures
“The extraction method was principal axis factoring with an oblique (promax with Kaiser normalization) rotation.”
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
“| TRU → PU→ ATT → BI | .03 | .06 | .16 | H4c |”
Reported 0.03 · recomputed 0.101556
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | .08 | .76 | .67 |”
§ Tables and Figures
“| Smart technologies have the features necessary to complete key tasks. (Competence) | -.03 | .87 | .72 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ [Figure 2 About Here]
“Regression estimates indicated that all four proposed predictors of AI voice assistant usage intention have statistically significant direct and indirect effects on the dependent variables.”
§ Discussion
“Although trust does not directly affect usage intention, the construct appears to be pivotal in improving user perception of the utility of voice assistants and in building positive attitudes toward them.”
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 5×
“The original TAM proposed perceived usefulness and perceived ease of use to be the two determinants of future usage intention (Davis, 1989).”
BackgroundMethodInfluentiallavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955 · cited 1×
“The analysis was conducted in R using the lavaan package (Rosseel, 2012).”
MethodA Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · cited 3×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
BackgroundMethodAn integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736 · cited 7×
“In interpersonal relationships, this corresponds to the consistency of behaviors associated with adherence to a set of norms (Mayer et al., 1995).”
BackgroundMethodInfluentialArtificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 1×
“…growing fear that our reliance on AI comes at the cost of human agency, and that fear is further stoked by the black-box nature of machine learning algorithms, which generate a response that mimics human response without adequate explanation of the underlying process (Barredo Arrieta et al., 2020).”
BackgroundI.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Not So Different After All: A Cross-Discipline View Of Trust
Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998) · Academy of Management Review · doi:10.5465/amr.1998.926617 · Cited by 10,274 · cited 1×
“Trust is defined as “a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another” (Rousseau et al., 1998, p. 395).”
BackgroundTrust and TAM in Online Shopping: An Integrated Model1
Gefen, D., Karahanna, E., & Straub, D. W. (2003) · MIS Quarterly · doi:10.2307/30036519 · Cited by 8,635 · cited 6×
“This study follows the hypotheses tested by other TAMrelated studies that have proposed trust as an antecedent of perceived usefulness and subsequent to perceived ease of use while having a direct effect on attitude (Gefen et al., 2003; Kim, 2012; Wu et al., 2011).”
MethodResultInfluentialTechnology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · cited 1×
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
Background
Research works printed but not linked: 3
- AI HLEG (2019). Ethics guidelines for trustworthy AI. European Commission. https://data.europa.eu/doi/10.2759/346720 · DOI
- Shin D, 2021, New Media & Society, 1
- Wu J., 2007, Journal of Electronic Commerce Research, 8, 128
Cited by
Sorted by citations
Items 801–900 of 966
Chen, B., Deng, R., & Ahmed, S. (2026) · Journal of Information Technology & Politics · doi:10.1080/19331681.2026.2682924
Gómez‐Rey, P., Sánchez, S. A., Alamäki, A. V., & Schlögl, S. (2026) · Education Sciences · doi:10.3390/educsci16071024
“The items were grouped into six dimensions according to their theoretical origin: AI awareness, AI usage, and AI evaluation were taken from B. Wang et al. (2023); AI ethics from Carolus et al. (2023); AI trust from Choung et al. (2023); and digital skills from Vitezi´c and Peri´c (2024).”
From Interaction to Involution: Human-AI Interaction and Employee Work Involution
Cheng, S., & Fan, Q. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2691241
Enhancing Digital Collaboration Through Human-Centered AI and Machine Learning
Kumar, K. G., Sangamithra, T., Balamurugan, K., Periyasamy, S., Keerthana, N. V., & Vijay, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8217-3.ch004
A sensemaking approach for AI adoption: the role of AI framing and digital culture
Liu, S. Y. H., Gustafson, B., & Gustafson, M. (2026) · Marketing Intelligence & Planning · doi:10.1108/mip-03-2025-0276
Meta-structural intelligence: AI as architect of organizational meta-structures
Mahmood, A. (2026) · Innovation The European Journal of Social Science Research · doi:10.1080/13511610.2026.2694988
Wei, S., Chen, D., Yu, S., Ren, Z., An, W., Liu, Z., & Pusetso, T. J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2693617
Duncan, E. A., Agyapong, K., & Acheampong, A. (2026) · Circular Economy and Sustainability · doi:10.1007/s43615-026-01048-1
“The finding aligns with Choung et al. (2023), who determined that trust in AI increases its perceived compatibility and relative advantage.”
InfluentialNejat, S., Goudarzi, F. A., Khanjanzadeh, N., & Firoozjaei, A. A. (2026) · Journal of Islamic marketing · doi:10.1108/jima-08-2025-0524 · Cited by 1
Lee, J. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7049794
The Relationship Between Personal Pronouns Used by AI and AI Trust of Users
Wang, D., Fu, Y., Li, L., & Song, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2694630
Engineering Trust in AI‐Enabled Systems: Lessons from the Trusted AI Challenge
Saliba, S., Meno, E., Lucero, S., Cody, T., Lau, N., Lipizzi, C., Moore, H., Panchal, J. H., Shetty, S., Szajnfarber, Z., et al. (2026) · Insight · doi:10.1002/inst.70057
Wu, C.-F., Lin, M., & Chiou, C. (2026) · Expert Systems · doi:10.1111/exsy.70355
Munandar, D., Narimawati, U., Nusa, I. B. S., -, T., & Winanti, M. B. (2026) · Pakistan Journal of Commerce and Social Sciences · doi:10.64534/commer.2026.684 · Cited by 1
User Adoption and Interaction with Generative AI Chatbots Embedded in Social Media Platforms
Shrestha, S., Ranjbar, M. J., Karakaya, K., & Gilbert, S. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31407-9_24
Yao, Y., Zhou, K., & Liu, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30089-8_14
Patient-Engaged AI: The Nursing Path to Health Equity
Milo, R. B., Etland, C., Martínez, N., Scott, S., Leon, C. D., Calero, P., Nibbelink, C. W., Georges, J. M., & Connelly, C. D. (2026) · SAGE Open Nursing · doi:10.1177/23779608261466379
“Trust is an individual’s psychological state and willingness to accept vulnerability grounded in their optimistic expectations about AI’s purpose and usefulness (Choung et al., 2022).”
Şahin, M., Gültekin, M. H., Erdoğdu, F., & Cesur, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2719186
“It Feels like a Friend, Sometimes More”: A Qualitative Study of Affective Human–AI Relationships
Cheng, N., & Yu, R. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29903-1_24
Development and Validation of AI Acceptance Scale (AI-AS) for Learners in Higher Education
Kayalı, B., Yavuz, M., Üstün, A. G., Uçar, H., Erdoğdu, E., & Bozkurt, A. (2026) · TechTrends · doi:10.1007/s11528-026-01209-x
Alubthane, F. O. (2026) · Education Sciences · doi:10.3390/educsci16071118
“The multidimensional nature of this trust encompasses perceived capability, perceived benevolence, and perceived integrity, each representing a distinct evaluative orientation toward the AI as a knowledge source (Choung et al., 2023).”
The effect of transformational leadership on employees’ generative AI usage intentions
Lim, S., & Kim, B. (2026) · Strategy and Leadership · doi:10.1108/sl-04-2026-0174 · Cited by 1
Sibug, V. B., Miller, J. C., Grume, J. C., Bansil, J. A., Nacianceno, M. C. B., Salenga, J. L., Parreño, E. B., & Miranda, J. P. P. (2026) · Physical Education of Students · doi:10.15561/20755279.2026.0401
“Following prior findings that trust predicts behavioral intention independent of perceived usefulness and ease of use [20, 27], a direct path from Trust in AI to Behavioral Intention was included alongside the indirect paths through PU and EOU.”
InfluentialBaskaran, R., Al-Gholmy, O., Kamal, W. K., Nedbal, C., Panthier, F., Traxer, O., Bhojani, N., Philip, J., & Somani, B. K. (2026) · Urology Annals · doi:10.4103/ua.ua_42_26
“[32,33] This study is the first to assess the quality and readability of online and AI-generated health information for patients undergoing SWL. DISCERN scores for both websites and AI-generated leaflets fell within the “good” category.”
Exploring Human Trust in Software Robots for Robotic Process Automation
Agostinelli, S., Bisconti, P., Casciani, A., Marrella, A., Rehse, J.-R., Angelini, M., Asatiani, A., Axmann, B., Czarnecki, C., del–Río–Ortega, A., et al. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-026-01007-y
Dual Identity, Dual Acceptance Path of AI Influencers: As Social Actors and Technological Systems
Liu, Z., Mundel, J., & Huddleston, P. (2026) · Journal of Global Marketing · doi:10.1080/08911762.2026.2687783
ERP users intention to use artificial intelligence and automation instruments in Romanian businesses
Simionescu, C., Siriteanu, A.-A., DĂNILOAIA, D.-F., Dospinescu, O., Olariu, M.-E., & Iftene, A. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2700942 · Cited by 1
Herani, R., Angela, J., & Virgosita, R. (2026) · Journal of Developmental Entrepreneurship · doi:10.1142/s1084946726500135
Kumar, S., Singh, V., & Pandey, V. (2026) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-01-2025-0025
Digital inclusion and algorithmic bias: auditing AI systems in HR for gender-neutral outcomes
Kumar, R. P., Neerupa, C., & Murali, S. (2026) · Evidence-based HRM a Global Forum for Empirical Scholarship · doi:10.1108/ebhrm-02-2026-0052
Wang, M., & Yu, T. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1903629
“Choung et al. (2023) provided empirical validation of this distinction within a TAM framework, demonstrating that both system-like and human-like trust significantly shape AI acceptance, with human-like trust playing a particularly critical role in fostering trusting intentions.”
Yang, W., Ma, Y., & Yang, Q. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1886459
“Whether a digital orientation is actually enacted depends on the cognitive and affective conditions surrounding decision-makers, with trust in AI systems being a particularly relevant condition in our setting (Choung et al., 2023; Vanneste and Puranam, 2025).”
Trust the AI, Doubt Yourself: The Effect of Urgency on Self-Confidence in Human-AI Interaction
Shajari, B., Liu, X., Dagenais, K., & Dávid, I. (2026) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3803437.3806712
Alameri, M. M. O., Zainuddin, M. T., & Nawang, W. R. W. (2026) · International Journal of Law Government and Communication · doi:10.35631/ijlgc.1144015
Exploring Perceptions of Robo-advisors for Personal Investing
Cooney, O., Leonard, S. J., Peña, P. R., Hannah, J., & Cowan, B. R. (2026) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3816046.3816211
“As is the case in financial services [38] as well as in many AI based applications [15], trust has an important role in users’ attitudes towards and intentions to use robo-advisors [44, 61].”
User Acceptance of AI Photo Editting on Platform X: Technology Acceptance Model TAM
Haykal, M., Andrakusuma, K. W., Wijaya, M. I., & Hidayat, M. A. (2026) · International Symposium on AI-Driven Engineering Systems (ISADES) · doi:10.1109/isades69945.2026.11608108
“Recommendations from social circles, surrounding environments, and broader technological trends significantly influence how useful individuals perceive an AI system to be [12], [13], [15].”
Wiryadinata, D. N., Hutagalung, G. M., Steven, & Zamasi, H. C. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715061
“Consumers' attitudes about AI voice assistants are meaningfully outcomed by trust, with attitude and perceived utility serving as important channels[11].”
Wang, S., Sun, X., Ni, S., Wu, M., & Hu, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-61031-6 · Cited by 1
“Since the research scenario involves voice to algorithmic leader, and research has shown that people’s trust in AI technology can enhance their acceptance of AI technology 95 and intention of usage AI technology 96,97 .”
Mohamed, M. E., & Aljuaid, F. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108274
A Product Management Framework for AI-Driven Digital Platform Transformation
Chavan, B. (2026) · IEEE International Conference on Signals and Systems (ICSigSys) · doi:10.1109/icsigsys71160.2026.11613898
Wang, Y., & Tang, Y. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103109
A UTAUT-Based Study of AI Acceptance and Dependency in L2 Writing Among Chinese EFL Students
Sun, W., Liu, Y., & Shin, Y. (2026) · SAGE Open · doi:10.1177/21582440261422350
Salad, A. M., Omar, M. M., Ahmed, A. B., Yusuf, S. A., MOHAMED, A. A., & Abdalla, H. H. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2707679
Wang, T., & Bashir, M. N. (2026) · Computers · doi:10.3390/computers15090551
Theobald, E., & Bohe, J. (2026) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-026-01312-w
Generative AI Adoption in Local Government: A PLS-SEM and fsQCA Study of Vietnamese Civil Servants
Duy, P. N., Ruangthamsing, C., Kamnuansilpa, P., Lowatcharin, G., & Setthasuravich, P. (2026) · Administrative Sciences · doi:10.3390/admsci16080365
“When employees trust GenAI outputs, they are more likely to integrate the technology into work processes without extensive verification, thereby enhancing perceived usefulness (Lankton et al., 2015; Choung et al., 2023; Hmoud & Várallyai, 2020).”
Advancing sustainable construction: A trust-enhanced model of AI adoption and realised use
Regona, M., Yiğitcanlar, T., Hon, C. K. H., & Teo, M. (2026) · Sustainable Cities and Society · doi:10.1016/j.scs.2026.107755
Zang, X., Li, H., Zhang, Z., & Yoo, B. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7199728
Vertrauen als Erfolgsfaktor für die Einführung von Technologien und Innovationen im Gesundheitswesen
Gille, F., & Starke, G. (2026) · Edition HMD · doi:10.1007/978-3-658-51795-3_29
AI for good: how does quality and fairness of AI affect employee learning from AI
Wang, Y., & Yuan, X. (2026) · Journal of Business Research · doi:10.1016/j.jbusres.2026.116442
Mittal, P., Kaur, A., Malhotra, S., Kaur, B., & Kaur, A. (2026) · Mental Illness · doi:10.1155/mij/8325823
The Effect of Belief in a Just World on Acceptance versus Fear of Artificial Intelligence
Kim, H., & Kim, P. (2026) · International Journal of Humanities and Applied Social Science · doi:10.33642/ijhass.v11n7p1
ChatGPT-Mediated Exam Preparation Among Pre-service English Teachers: A Qualitative Case Study
Çetinkaya, Ş. E., & Çepni, S. B. (2026) · Journal of Language Research · doi:10.51726/jlr.1891228
Mejri, R., & Skhiri, S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1752349
When Appearance Outweighs Function: Collectivism and Preferences for AI Design
Wang, S., Xu, T., & Sui, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2708453
Ahmed, H., Riaz, H., Botchway, R. K., & Jibril, A. B. (2026) · Cogent Business & Management · doi:10.1080/23311975.2026.2707799
Zhang, X., Qiao, Y., Wang, Y., Gao, Y., Zhang, Y., Li, A., & Li, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2707715
Zhu, J., Chen, J., Covarrubias, M., Li, D., & Wei, W. (2026) · Sustainability · doi:10.3390/su18157986 · Cited by 1
How AI induces professional identity threats: A survey study with public relations professionals
Qu, Y., Morehouse, J., Lu, S., & Ahmad, T. (2026) · Public Relations Review · doi:10.1016/j.pubrev.2026.102745
Jia, C., Li, H., Wu, L., Xu, Z., Shen, L., Liu, Y., & Wang, D. (2026) · BMC Psychology · doi:10.1186/s40359-026-05339-y
How employees' AI use contributes to digital-enabled innovative performance
Ma, L., Qi, Z., Zhang, X., & Hao, F.-F. (2026) · Internet Research · doi:10.1108/intr-03-2025-0424
Wang, E. J., Berthon, P. R., & John, J. (2026) · Journal of Services Marketing · doi:10.1108/jsm-12-2025-0888
From traditional feedback literacy to AI feedback literacy: the mediating role of AI acceptance
Yıldız, M., Yazıcı, Z., & Peker, A. (2026) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2026.2714393
Reño, A. B., Sasutana, A., Ticmon, S. L., Torralba, M., & Villa, K. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631373
“These results align with studies by [14], [15], and [16].”
Najati, I. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103400
Li, Z., Ong, Y. T., & Yuen, K. F. (2026) · International Trade Politics and Development · doi:10.1108/itpd-05-2026-0036
Promise and Perils of AI: Social and Economic
Malieckal, M., & Gurtoo, A. (2026) · Springer eBooks · doi:10.1007/978-3-032-22368-5_17
Arslan, N., Çelik, Ö. M., Güler, Ü., & Ekici, E. M. (2026) · BMC Public Health · doi:10.1186/s12889-026-28997-4
“In particular, factors such as perceived benefit, ease of use, trust, and individual attitudes are reported to be among the key determinants shaping the acceptance of artificial intelligence technologies [16, 17].”
Liu, J., Zhang, C., & Gu, L. (2026) · doi:10.1109/iccms69991.2026.11642771
“Traditionally interpersonal in nature, trust remains fundamental for establishing reciprocal relationships[28].”
Chu, X., & Dai, H. (2026) · Educational Psychology · doi:10.1080/01443410.2026.2717335
Xu, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7294156
Trust in artificial intelligence to extract geospatial features for topographic databases
Koski, C., Kettunen, P., Tenkanen, H., & Oksanen, J. (2026) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2026.2716279
Chang, W., & Park, J. (2026) · Journal of Marketing Management · doi:10.1080/0267257x.2026.2715539
Bentley, S. V., EVANS, D. B., & McGrath, M. J. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45279
Imperial, R. M. A., & Artiola, A. S. (2026) · Psychology and Education A Multidisciplinary Journal · doi:10.70838/pemj.610409
Park, S., Song, Y., & Cho, S. (2026) · IEEE Access · doi:10.1109/access.2026.3724790
“As AI systems have become more autonomous and complex, this discussion has expanded to human–AI trust, where users must evaluate systems that operate with limited transparency and probabilistic behavior [25], [26], [27], [28], [29].”
Artificial Intelligence (AI) in Forensic Psychology: An Umbrella Review of Potentials and Pitfalls
Guevarra, Y. A., Hisham, E. M., & Hartanto, A. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/6547352
Predictors of public support for AI in policing
Güler, A., Kula, S., & Boke, K. (2026) · Policing An International Journal · doi:10.1108/pijpsm-04-2026-0115
Konti, E., & Kotsis, K. Τ. (2026) · International Journal of Changes in Education · doi:10.29333/ijce/19185
“Trust is especially critical in situations where users may suffer uncertainty or have limited agency in technology decisions (Choung et al., 2023; Zerilli et al., 2022).”
Kocak, O., İdil, Ş., & Aydoğan, M. (2026) · Research Square · doi:10.21203/rs.3.rs-9615155/v1
A Task–Technology Fit Perspective on Trust and User Satisfaction in Artificial Intelligence
Purwita, A. W., Bisma, R., Palupi, G. S., Indriyanti, A. D., Suryanti, S., Rahmaningrum, A., & Li, L. (2026) · E3S Web of Conferences · doi:10.1051/e3sconf/202673603004
“Recent research suggests that AI trust depends on the quality of technology and the performance of the system rather than the situation (task) factors [7–9].”
Huang, Q., Xu, J., Zhang, R., & Song, G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1892349
“Numerous studies indicate that perceived usefulness represents a key determinant in users’ continued engagement with technology (Choung et al., 2023).”
Role of AI dependency in shaping 21st century skills in higher education with AI trust as a mediator
Akter, M. S., Sultana, F., Alam, S., Rupa, R. A., & Sultana, N. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02056-8
Yao, X., Xu, F., & Shao, Y. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2746841
Stokseth, Ø., Sætra, H. S., Nordrum, E. I., & Borgebund, H. (2026) · Springer proceedings in business and economics · doi:10.1007/978-3-032-20432-5_10
Wang, F., Huang, Y., Jiang, Q., & Chen, S. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2705666
HEAR what AI says: simulated empathy and supportive ChatGPT interaction for emotional well-being
Dung, P. T. P., Hoang, L. C., Phuc, K. N., Anh, T. L. D., & Thu, H. P. T. (2026) · Behaviour and Information Technology · doi:10.1080/0144929x.2026.2722218
Zhou, Y., & Du, T. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103526
Yan, J., Abideen, A. Z., Chen, X., & Xiang, W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7365850
Modeling Behavioral Intention to Use Artificial Intelligence Applications Using SmartPLS Approach
Alamsyah, D. P., Bako, D. S., Cahya, M., & Tarikh, A. M. A. (2026) · IEEE International Conference on Instrumentation, Networks, Future Sustainability, Information Theory, Transportation, and Systems (INFINITYS) · doi:10.1109/infinitys69204.2026.11666033
“Generally, to explain the level of student trust in AI chatbots, several measures can be used, including the level of benevolence, the level of integrity, and the level of competence in AI chatbots [9].”
InfluentialPeng, Z., Wang, G., Ma, H., Qiu, H., & Chen, H. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663860
“Choung et al. (2022) after revising and verifying that the requirements are met [26].”
Wu, M., Cheo, J., & Yuen, K. F. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124838
Wang, G., Peng, Z., Qiu, H., Ma, H., & Chen, H. (2026) · International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel70250.2026.11663875
“We define AI trust as learners’ willingness to rely on AI tools based on their functionality and human-like characteristics [14].”
Are virtual streamers more trustworthy? An ERP study of trust in E-commerce livestreaming
Huo, J., Sun, F., Liu, T., & Fang, L. (2026) · Current Psychology · doi:10.1007/s12144-026-09984-9
“Such trust shapes whether users accept, rely on, and continue to interact with AI systems (Choung et al., 2023; Glikson & Woolley, 2020).”
Li, Z., Han, J., Wu, L., & Xiaogang, W. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1731653
“In AI contexts, perceived usefulness reflects whether users believe AI can assist them in accomplishing tasks more efficiently, optimizing decision-making, or enhancing experiences (Choung et al., 2023).”
Mızrak, F., Gözde, M. E. R. T., Erkasap, A., Özcan, A., & Mert, M. (2026) · Journal of Intelligence · doi:10.3390/jintelligence14090196
“Earlier trust-in-automation research shows that trust is central to how people interact with automated systems (Jian et al., 2000), and more recent work confirms that trust plays an important role in AI acceptance and use (Choung et al., 2023; Glikson & Woolley, 2020).”
AYHAN, E. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1906982 · Cited by 1
“Recently, TAM has expanded to explore the adoption of AI-assisted education tools, incorporating concepts such as perceived fairness, trust, and subjective norm (Choung et al., 2024; Zou and Huang, 2023).”
Toudjui, S., Tang, J., Akhter, H., Cho, E., Côté, É., & Plaks, J. E. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02150-x
“This creates conditions of uncertainty in which trust, understood as the willingness to take risks despite the uncertainty [13, 14], serves as an important psychological precursor to AI use [15].”
Metadata
- Authors
- Choung, H., David, P., & Ross, A.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Choung, Hyesun
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2022-04-20
- Volume
- 39
- Issue
- 9
- Pages
- 1727-1739
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4221014470
- Semantic Scholar
- f281ad6573ed8ea7fb3116a3747b0e80ef522ea9
- Cited by
- 963
- References
- 62
- Citations by year
- 2026: 402 · 2025: 360 · 2024: 142 · 2023: 54 · 2022: 5
- FWCI
- 117.34
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- trust in AI, technology acceptance, intention to use, voice assistants, perceived usefulness, perceived ease of use, attitudes toward technology, trustworthy AI, technology acceptance model
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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